Purpose

This bill would prohibit using automated decision systems to set prices or wages based on surveillance data about a person. Its goal is to stop surveillance-based price and wage discrimination and to protect consumers and workers from algorithmic decisions that rely on personal observations or characteristics.

Key Definitions (simplified)

• Automated decision system: a computer system or program that helps or replaces human decisions, including tools built with machine learning, statistics, or AI.
• Surveillance data: information gathered by watching or collecting data about a person, including personal characteristics, behaviors, or biometrics, and even data that is bought or otherwise obtained.
• Price: all money and terms charged for a good or service, including related fees.
• Wage: the compensation a worker receives, including pay rate, bonuses, scheduling, and other terms affecting earnings.
• Consumer: someone who buys goods for personal use.
• Worker: a person who performs work for an employer (employee, contractor, or similar).
• Biometrics: identifying information from a person’s body or behavior (e.g., fingerprints, voiceprints, facial features, iris data, genetic information).
• Personal characteristics: traits that can include immutable factors (like race or eye color) and other identifying details (like address or Social Security number).
• Insurer: a licensed insurance company.
• Individualized: specific to a person or a particular group with shared characteristics.
• Surveillance-based price discrimination: using an automated system to set individualized prices based on surveillance data.
• Surveillance-based wage discrimination: using an automated system to set individualized wages based on surveillance data.

Prohibited Practices

• Price discrimination: It is prohibited to use an automated decision system to set prices based on surveillance data about a consumer.
• Wage discrimination: It is prohibited to use an automated decision system to set wages based on surveillance data about a worker.

Exceptions and Allowed Practices

• Price discrimination exceptions: 1) Differential prices justified by differences in the cost to provide the good or service. 2) Differential prices that reflect discounts offered to all consumers on equal terms, provided the terms are public and the discount is accessible to the public, and the discount rewards membership in a recognized group (e.g., active military, veterans, teachers, students, seniors). 3) Insurers may input only risk-relevant data into the automated decision system that informs pricing for an insurance policy or contract.
• Wage discrimination exceptions: 1) Wages may be individualized if based solely on data about the specific tasks the worker was hired to perform or differences in the cost to the employer to have the worker provide labor. 2) Before hiring, an employer must clearly disclose to all workers what data is considered and how the automated decision system uses that data to set wages.

Publication of Procedures (Transparency Requirements)

• Employers or others using an automated decision system for wages or prices must publish reasonable procedures to: 1) Ensure the data used by the system is accurate. 2) Allow consumers or workers to correct or challenge the accuracy of data used by the system. 3) Inform consumers or workers about what data is considered and how the system uses that data to set specific prices or wages.

Significance and Potential Impact

• The bill introduces new protections against algorithmic pricing and pay decisions that rely on surveillance data.
• It requires transparency about what data is used and how it influences prices and wages.
• It creates explicit exceptions for cost-based pricing, public-discount terms, and insurer-sensitive data.
• It aims to reduce discriminatory or opaque pricing and pay practices tied to personal data and biometrics.
• Enforcement details and penalties are not specified in the provided text.

Relevant terms surveillance-based price discrimination; surveillance-based wage discrimination; automated decision system; surveillance data; price; wage; consumer; worker; biometrics; personal characteristics; cost differences; discounts; insurer; pre-hiring disclosure; data accuracy; transparency; data correction.